Purpose: To evaluate in clinical use a practical iterative deconvolution method to enhance contrast and image resolution in digital breast tomosynthesis. A novel rapidly converging, iterative deconvolution algorithm for improving the quantitative accuracy of previously reconstructed breast images by commercial breast tomosynthesis system is demonstrated. Materials and Methods: The method was tested on phantoms and clinical breast imaging data. Data acquisition was performed on a commercial Hologic Selenia Dimensions digital breast tomosynthesis system. The method was applied to patient breast images previously processed with Hologic Selenia conventional and C-View software to determine improvements in resolution and contrast to noise ratio. Results: In all of the phantom and patients' breast studies the post-processed images proved to have higher resolution and contrast as compared with images reconstructed by Hologic methods. In general, the values of CNR reached a plateau at around 8 iterations with an average improvement factor of about 1.8 for processed Hologic Selenia images. Improvements in image resolution after the application of the method are also demonstrated. Conclusions: A rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach that operates on patient DICOM images has been used for quantitative improvement in digital breast tomosynthesis. The method can be applied to clinical breast images to improve image quality to diagnostically acceptable levels and will be crucial in order to facilitate diagnosis of tumor progression at the earliest stages. The method can be considered as an extended blind deblurring (or Richardson-Lucy like) algorithm with multiple resolution levels.